How to get shape of previous layer and pass it to next layer?

Question

I want to take the shape of Input data which is passed to Input layer with (None,) shape, and use it in a for loop for some purpose.

Here's part of my code implementation:

lst_rfrm = []
Inpt_lyr = keras.Input(shape = (None,))
for k in range(tm_stp):
  F = keras.layers.Lambda(lambda x, i, j: x[:, None, j : j + i])
  F.arguments = {'i' : sub_len, 'j' : k}
  tmp_rfrm = F(Inpt_lyr)
  lst_rfrm.append(tmp_rfrm)
cnctnt_lyr = keras.layers.merge.Concatenate(axis = 1)(lst_rfrm)
#defining other layers ...

because the Input shape is (None,), I don't know what to give to for loop as range( at the code i describe it with 'tm_stp'). how can i get the shape of the input layer (the data that is passed to input layer) in this situation? any help is deeply appreciated

Answer 1

You can try a different type of loop. It seems you are trying sliding windows, right? You don't know the "length" to run, but you know the window size and how much of the borders to remove... so....

This function gets the slices following that principle:

windowSize = sub_len
def getWindows(x):
    borderCut = windowSize - 1 #lost length in the length dimension

    leftCut = range(windowSize) #start of sequence
    rightCut = [i - borderCut for i in leftCut] #end of sequence - negative
    rightCut[-1] = None #because it can't be zero for slicing

    croppedSequences = K.stack([x[:, l: r] for l,r in zip(leftCut, rightCut)], axis=-1)
    return croppedSequences

Running test:

from keras.layers import *
from keras.models import Model
import keras.backend as K
import numpy as np

windowSize = 3
batchSize = 5

randomLength = np.random.randint(5,10)
inputData = np.arange(randomLength * batchSize).reshape((batchSize, randomLength))

def getWindows(x):
    borderCut = windowSize - 1

    leftCut = range(windowSize)
    rightCut = [i - borderCut for i in leftCut]
    rightCut[-1] = None

    croppedSequences = K.stack([x[:, l: r] for l,r in zip(leftCut, rightCut)], axis=-1)
    return croppedSequences

inputs = Input((None,))
outputs = Lambda(getWindows)(inputs)
model = Model(inputs, outputs)

preds = model.predict(inputData)

for i, (inData, pred) in enumerate(zip(inputData, preds)):
    print('sample: ', i)
    print('input sequence: ', inData)
    print('output sequence: \n', pred, '\n\n')